The Grain Impact Analyser and Dust Accumulator (GIADA) Experiment for the Rosetta Mission: Design, Performances and First Results

The Grain Impact Analyser and Dust Accumulator (GIADA) onboard the ROSETTA mission to comet 67P/Churyumov–Gerasimenko is devoted to study the cometary dust environment. Thanks to the rendezvous configuration of the mission, GIADA will be plunged in the dust environment of the coma and will be able to explore dust flux evolution and grain dynamic properties with position and time. This will represent a unique opportunity to perform measurements on key parameters that no ground-based observation or fly-by mission is able to obtain and that no tail or coma model elaborated so far has been able to properly simulate. The coma and nucleus properties shall be, then, clarified with consequent improvement of models describing inner and outer coma evolution, but also of models about nucleus emission during different phases of its evolution. GIADA shall be capable to measure mass/size of single particles larger than about 15 μm together with momentum in the range 6.5 × 10⁻¹⁰ ÷ 4.0 × 10⁻⁴ kg m s⁻¹ for velocities up to about 300 m s⁻¹. For micron/submicron particles the cumulative mass shall be detected with sensitivity 10⁻¹⁰ g. These performances are suitable to provide a statistically relevant set of data about dust physical and dynamic properties in the dust environment expected for the target comet 67P/Churyumov–Gerasimenko. Pre-flight measurements and post-launch checkouts demonstrate that GIADA is behaving as expected according to the design specifications. The International GIADA Consortium (I, E, UK, F, D, USA).     

Beamspace ML bearing estimation incorporating low-angle geometry

A problem in low-angle radar tracking, namely, bearing estimation in the presence of a strong specular multipath component that arrives within the beamwidth of the direct path signal, is studied. Three-dimensional beamspace domain maximum likelihood (3D-BDML) is a computationally simple ML bearing estimation algorithm applicable in this scenario which operates in a 3-D beamspace. A variation of 3D-BDML incorporating the multipath geometry as a priori information is presented. In symmetric 3D-BDML the pointing angle of the center beam is equal to the bisector angle between the direct path ray and the image ray, which may be estimated a priori given only the radar height and the target range. The effect of the inclusion of a priori information on the performance of 3D-BDML is analyzed in terms of the dependence on the relative phase difference between the direct and specular path signals, the sensitivity to error in the bisector angle estimate, and the results of operation when no specular multipath component is present in the data. In addition, computationally simple schemes for coherently incorporating multifrequency data into 3D-BDML are investigated     

Science operations planning expertise: A neglected component of mission design

In this paper, Science Operations Planning Expertise (SOPE) is defined as the expertise that is held by people who have the two following qualities. First they have both theoretical and practical experience in operations planning, in general, and in space science operations planning in particular. Second, they can be used, on request and at least, to provide with advice the teams that design and implement science operations systems in order to optimise the performance and productivity of the mission. However, the relevance and use of such SOPE early on during the Mission Design Phase (MDP) is not sufficiently recognised. As a result, science operations planning is often neglected or poorly assessed during the mission definition phases. This can result in mission architectures that are not optimum in terms of cost and scientific returns, particularly for missions that require a significant amount of science operations planning. Consequently, science operations planning difficulties and cost underestimations are often realised only when it is too late to design and implement the most appropriate solutions. In addition, higher costs can potentially reduce both the number of new missions and the chances of existing ones to be extended. Moreover, the quality, and subsequently efficiency, of SOPE can vary greatly. This is why we also believe that the best possible type of SOPE requires a structure similar to the ones of existing bodies of expertise dedicated to the data processing such as the International Planetary Data Alliance (IPDA), the Space Physics Archive Search and Extract (SPASE) or the Planetary Data System (PDS). Indeed, this is the only way of efficiently identifying science operations planning issues and their solutions as well as of keeping track of them in order to apply them to new missions. Therefore, this paper advocates for the need to allocate resources in order to both optimise the use of SOPE early on during the MDP and to perform, at least, a feasibility study of such a more structured SOPE.     

Magnetospheric Science Objectives of the <Emphasis Type="Italic">Juno</Emphasis> Mission

In July 2016, NASA’s Juno mission becomes the first spacecraft to enter polar orbit of Jupiter and venture deep into unexplored polar territories of the magnetosphere. Focusing on these polar regions, we review current understanding of the structure and dynamics of the magnetosphere and summarize the outstanding issues. The Juno mission profile involves (a) a several-week approach from the dawn side of Jupiter’s magnetosphere, with an orbit-insertion maneuver on July 6, 2016; (b) a 107-day capture orbit, also on the dawn flank; and (c) a series of thirty 11-day science orbits with the spacecraft flying over Jupiter’s poles and ducking under the radiation belts. We show how Juno’s view of the magnetosphere evolves over the year of science orbits. The Juno spacecraft carries a range of instruments that take particles and fields measurements, remote sensing observations of auroral emissions at UV, visible, IR and radio wavelengths, and detect microwave emission from Jupiter’s radiation belts. We summarize how these Juno measurements address issues of auroral processes, microphysical plasma physics, ionosphere-magnetosphere and satellite-magnetosphere coupling, sources and sinks of plasma, the radiation belts, and the dynamics of the outer magnetosphere. To reach Jupiter, the Juno spacecraft passed close to the Earth on October 9, 2013, gaining the necessary energy to get to Jupiter. The Earth flyby provided an opportunity to test Juno’s instrumentation as well as take scientific data in the terrestrial magnetosphere, in conjunction with ground-based and Earth-orbiting assets.